Optimization of manual fabric-cutting process in apparel manufacture using genetic algorithms

In apparel manufacturing, experience and subjective assessment of production planners are used quite often to plan the production schedules in their fabric-cutting departments. The quantities of cut-pieces produced by fabric-cutting departments based on these non-systematic schedules cannot fulfil the cut-piece requirements of the downstream sewing lines and minimize the makespan. This paper proposes a genetic algorithms (GAs) approach to optimize both the cut-piece requirements and the makespan of the conventional fabric-cutting departments using manual spreading and cutting methods. An optimization model for the manual fabric cutting process based on GAs was developed. Two sets of production data were collected to validate the performance of the model and the experimental results were obtained. From the results, it can be found that both the makespan and cut-piece fulfilment rates are improved in which the latter is improved significantly.